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- W4312250897 abstract "In this paper, we formulate the extractive summarization as a new paradigm, in which this task is regarded as node pruning and aims to retain a set of nodes and prune other irrelevant nodes. Instead of directly modeling the complicated dependency between sentences, we decouple the one-step summarization objective into multi pruning steps. Intuitively, pruning nodes with low salience then pruning redundant nodes is a similar scheme with hand-craft summary generation, which makes the summary generation more interpretable and effective. In this way, we propose a novel method for summarization called Prun-Sum, which contains salience pruning and redundancy pruning. Our pruning approach is further enhanced with assistance of powerful heterogeneous graph, and prunes some nodes during the graph updating to avoid the influence of negative nodes on sentence relationship modeling. To decouple the salience and redundancy between the sentences, we explicitly designed different graph structures and labels for each pruning process. Experiments results on two benchmark datasets (CNN/DM and NYT50) show that the performance of our method outperforms several state-of-the-art methods." @default.
- W4312250897 created "2023-01-04" @default.
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- W4312250897 date "2022-07-18" @default.
- W4312250897 modified "2023-09-27" @default.
- W4312250897 title "Decoupled Extractive Summarization as Graph Nodes Pruning" @default.
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- W4312250897 doi "https://doi.org/10.1109/ijcnn55064.2022.9891982" @default.
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